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CCLasso specifications

Information


Unique identifier OMICS_09143
Name CCLasso
Alternative name Correlation inference for Compositional data through Lasso
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
License GNU Lesser General Public License version 3.0
Computer skills Advanced
Stability Stable
Maintained Yes

Versioning


No version available

Maintainer


  • person_outline Minghua Deng

Publication for Correlation inference for Compositional data through Lasso

CCLasso citations

 (3)
library_books

Detection of stable community structures within gut microbiota co occurrence networks from different human populations

2018
PeerJ
PMCID: 5807925
PMID: 29441232
DOI: 10.7717/peerj.4303

[…] may therefore only apply to the current study. Furthermore, there are several alternative approaches that were not considered in the comparisons of Weiss and Van Treuren et al. such as SPIEC-EASI and CCLasso (; ). Further exploration of community detection using these methods is warranted. An approach to generate quantitative microbiota profiles using a sample’s total microbial cell count has also […]

call_split

Pediatric obesity is associated with an altered gut microbiota and discordant shifts in F irmicutes populations

2016
Environ Microbiol
PMCID: 5516186
PMID: 27450202
DOI: 10.1111/1462-2920.13463
call_split See protocol

[…] g the indicspecies package (De Caceres et al., ). Network analysis was performed for all OTUs present in at least 30% of samples as recommended in (Berry and Widder, ) using graphical lasso technique cclasso to mitigate biases associated with compositional data (Danaher et al., ). Network topological and node‐level properties were determined using the igraph package (Csardi, ) and networks were vi […]

library_books

Cross biome comparison of microbial association networks

2015
Front Microbiol
PMCID: 4621437
PMID: 26579106
DOI: 10.3389/fmicb.2015.01200

[…] r-wise Pearson or Spearman correlations (; ; in , coupled with random matrix theory), local similarity analysis (LSA; ; , ; ), compositionality-robust estimation of correlations (SparCC; , REBACCA; , CCLasso; ), Gaussian graphical models (; ), sparse regression (), and assessment of co-occurrence probability with the hypergeometric distribution for presence/absence data (; ). In food webs, Bayesia […]

Citations

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CCLasso institution(s)
LMAN, School of Mathematical Sciences; Beijing International Center for Mathematical Research; Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China; College of Global Change and Earth System Science, Beijing Normal University, Beijing, China; Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA; Center for Statistical Science, Peking University, Beijing, China

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